It’s increasingly the case that when we have problems to solve at work, we do so collectively. The best way to go about this collective endeavor, however, can often elude us.
Whilst a number of tools have emerged to support and facilitate group work, there remains a degree of doubt around just how much you should communicate, or how large your teams should be.
A recent paper set out to get to the heart of the matter and examine how groups tend to tackle complex problems effectively. The researchers modeled the way networked groups tackle problems, and found that whilst it’s quite straightforward to collaborate for mediocre outcomes, an exceptional outcome is much harder to achieve.
Striking a Balance
As with so much in life, they suggest the key is striking a balance between exploration of new ideas and the exploitation of those ideas. The key to achieving this balance is to match the learning style of the group with its network type.
In terms of learning styles, the authors compared two main forms. The first would see members using the best strategy developed by any member of the group, whilst the second would see the strategy selected by the most members chosen to develop further.
When the results were analyzed, there was a clear connection between the learning style adopted and the network structure of the group.
“When you copy the best solution your collaborators have found so far, you quickly pick up on promising solutions and explore less, risking zooming in on inferior solutions,” the authors say. “This fast strategy works well in less connected, slower networks that help strike the right balance between exploration and exploitation.”
If, instead, you’re picking the solutions most frequently chosen by the group, then you inevitably have to take a more patient approach to see what patterns emerge before adopting the most dominant solution. This encourages greater exploration and works best in a tightly connected network where information flows quickly.
Recognizing Your Team
The authors believe that the findings will enable us to build more effective teams in real world environments by guiding us in both the exploration and exploitation processes.
“Our study has broad implications for organizational learning, technological innovation, cultural evolution and the diffusion of innovations,” the authors write. “ These results highlight that interventions aimed at changing the social environment while disregarding social learning strategies might not produce the desired effects.”